Editorial Type:
Article
Article Type:
Research Article

How Predictability Depends on the Nature of Uncertainty in Initial Conditions in a Coupled Model of ENSO

Yun Fan Atmospheric, Oceanic, and Planetary Physics, Department of Physics, University of Oxford, Oxford, and Space Science Department, Rutherford Appleton Laboratory, Oxfordshire, United Kingdom

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M. R. Allen Atmospheric, Oceanic, and Planetary Physics, Department of Physics, University of Oxford, Oxford, and Space Science Department, Rutherford Appleton Laboratory, Oxfordshire, United Kingdom

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D. L. T. Anderson Atmospheric, Oceanic, and Planetary Physics, Department of Physics, University of Oxford, Oxford, and ECMWF, Reading, United Kingdom

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M. A. Balmaseda ECMWF, Reading, United Kingdom

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Print Publication:
15 Sep 2000
DOI:
https://doi.org/10.1175/1520-0442(2000)013<3298:HPDOTN>2.0.CO;2
Page(s):
3298–3313
Restricted access

Abstract

The predictability of any complex, inhomogeneous system depends critically on the definition of analysis and forecast errors. A simple and efficient singular vector analysis is used to study the predictability of a coupled model of El Niño–Southern Oscillation (ENSO). Error growth is found to depend critically on the desired properties of the forecast errors (“where and what one wants to predict”), as well as on the properties of the analysis error (“what information is available for that prediction”) and choice of optimization time. The time evolution of singular values and singular vectors shows that the predictability of the coupled model is clearly related to the seasonal cycle and to the phase of ENSO. It is found that the use of an approximation to the analysis error covariance to define the relative importance of errors in different variables gives very different results to the more frequently used “energy norm,” and indicates a much larger role for sea surface temperature information in seasonal (3–6-month timescale) predictability. Seasonal variations in the predictability of the coupled model are also investigated, addressing in particular the question of whether seasonal variations in the dominant singular values (the “spring predictability barrier”) may be largely due to the seasonality in the variance of SST anomalies.

* Current affiliation: Center for Ocean–Land–Atmosphere Studies, Institute of Global Environment and Society, Calverton Maryland.

Corresponding author address: Dr. Yun Fan, Center for Ocean– Land–Atmosphere Studies, 4041 Powder Mill Road, Suite 302, Calverton, MD 20705.

Abstract

The predictability of any complex, inhomogeneous system depends critically on the definition of analysis and forecast errors. A simple and efficient singular vector analysis is used to study the predictability of a coupled model of El Niño–Southern Oscillation (ENSO). Error growth is found to depend critically on the desired properties of the forecast errors (“where and what one wants to predict”), as well as on the properties of the analysis error (“what information is available for that prediction”) and choice of optimization time. The time evolution of singular values and singular vectors shows that the predictability of the coupled model is clearly related to the seasonal cycle and to the phase of ENSO. It is found that the use of an approximation to the analysis error covariance to define the relative importance of errors in different variables gives very different results to the more frequently used “energy norm,” and indicates a much larger role for sea surface temperature information in seasonal (3–6-month timescale) predictability. Seasonal variations in the predictability of the coupled model are also investigated, addressing in particular the question of whether seasonal variations in the dominant singular values (the “spring predictability barrier”) may be largely due to the seasonality in the variance of SST anomalies.

* Current affiliation: Center for Ocean–Land–Atmosphere Studies, Institute of Global Environment and Society, Calverton Maryland.

Corresponding author address: Dr. Yun Fan, Center for Ocean– Land–Atmosphere Studies, 4041 Powder Mill Road, Suite 302, Calverton, MD 20705.

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